A Research of Intelligent Parameters Searching in Small Data Sets
Increasingly competitive in a global economy, the lifecycle of product become shorter and shorter. How to shorten the time during research and development period, especially in the early stage in the industrial lifecycle is now an important issue. Unfortunately, lack of sufficient data always is a problem while acquiring knowledge in early stage. Therefore, this paper focuses on small data sets and further provides a systematic way for parameters searching. Our methodology is effectively selecting experimental parameter settings for redefining the boundary of parameter settings iteratively. There are four stages in our methodology: virtual sample generation, classification, selection and performance testing. In this paper, we design two experiments for verification four different selection mechanisms (RS, SVS, LVS, GVS). Furthermore, LVS and GVS mechanism will be discussed in the convergence experiment.
Classification IKDE Small Data Sets Problem SVM
Wei-Hua Andrew Wang Ya-Chun Chang Wen-Hsin Chen
Industrial Engineering & Enterprise Information Dep.,Tunghai University,Taichung,Taiwan
国际会议
厦门
英文
379-383
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）